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Dihydrouridine (D), a prevalent and evolutionarily conserved base in the transcriptome, primarily resides in tRNAs and, to a lesser extent, in mRNAs. Notably, this modification is found at position 2449 in the Escherichia coli 23S rRNA, strategically positioned near the ribosome's peptidyl transferase site. Despite the prior identification, in E. coli genome, of three dihydrouridine synthases (DUS), a set of NADPH and FMN-dependent enzymes known for introducing D in tRNAs and mRNAs, characterization of the enzyme responsible for D2449 deposition has remained elusive. This study introduces a rapid method for detecting D in rRNA, involving reverse transcriptase-blockage at the rhodamine-labeled D2449 site, followed by PCR amplification (RhoRT-PCR). Through analysis of rRNA from diverse E. coli strains, harboring chromosomal or single-gene deletions, we pinpoint the yhiN gene as the ribosomal dihydrouridine synthase, now designated as RdsA. Biochemical characterizations uncovered RdsA as a unique class of flavoenzymes, dependent on FAD and NADH, with a complex structural topology. In vitro assays demonstrated that RdsA dihydrouridylates a short rRNA transcript mimicking the local structure of the peptidyl transferase site. This suggests an early introduction of this modification before ribosome assembly. Phylogenetic studies unveiled the widespread distribution of the yhiN gene in the bacterial kingdom, emphasizing the conservation of rRNA dihydrouridylation. In a broader context, these findings underscore nature's preference for utilizing reduced flavin in the reduction of uridines and their derivatives. [ABSTRACT FROM AUTHOR]

Introduction: Dihydrouridine (D) is a conserved modification of tRNA among all three life domains. D modification enhances the flexibility of a single nucleotide base in the spatial structure and is disease- and evolution-associated. Recent studies have also suggested the presence of dihydrouridine on mRNA.Methods: To identify D in epitranscriptome, we provided a prediction framework named “DPred_3S” based on the machine learning approach for three species D epitranscriptome, which used epitranscriptome sequencing data as training data for the first time.Results: The optimal features were evaluated by the F-score and integration of different features; our model achieved area under the receiver operating characteristic curve (AUROC) scores 0.955, 0.946, and 0.905 for Saccharomyces cerevisiae, Escherichia coli, and Schizosaccharomyces pombe, respectively. The performances of different machine learning algorithms were also compared in this study.Discussion: The high performances of our model suggest the D sites can be distinguished based on their surrounding sequence, but the lower performance of cross-species prediction may be limited by technique preferences.

The formation of dihydrouridine from uridine substrate is catalysed by the human tRNA-dihydrouridine synthase (hDus2) enzyme. The abundance of dihydrouridine, possibly accumulated due to the aberrant function of hDus2, is linked with carcinogenesis. In this study, we focused on hDus2 enzyme, in hopes of discovering novel molecule with affinity for its tRNA binding site. Using the computational method, we performed virtual screening of a natural compound library (NPACT) with Autodock Vina, followed by validation using Smina and Idock. The top hits ZINC08219592, ZINC44387960, and ZINC95098958 were further investigated for their ADME properties to assess their potential as drug candidates. Additionally, the electronic structure properties of the lead molecules were investigated using Density Functional Theory (DFT). Our findings suggest that the identified natural molecules may act as potential hDus2 binders, opening new possibilities for the development of targeted anticancer drugs. This study provides a foundation for further research and the potential advancement of cancer therapeutics targeting on hDus2. [ABSTRACT FROM AUTHOR]

Dihydrouridine (D) is a modified pyrimidine nucleotide universally found in viral, prokaryotic, and eukaryotic species. It serves as a metabolic modulator for various pathological conditions, and its elevated levels in tumors are associated with a series of cancers. Precise identification of D sites on RNA is vital for understanding its biological function. A number of computational approaches have been developed for predicting D sites on tRNAs; however, none have considered mRNAs. We present here DPred, the first computational tool for predicting D on mRNAs in yeast from the primary RNA sequences. Built on a local self-attention layer and a convolutional neural network (CNN) layer, the proposed deep learning model outperformed classic machine learning approaches (random forest, support vector machines, etc.) and achieved reasonable accuracy and reliability with areas under the curve of 0.9166 and 0.9027 in jackknife cross-validation and on an independent testing dataset, respectively. Importantly, we showed that distinct sequence signatures are associated with the D sites on mRNAs and tRNAs, implying potentially different formation mechanisms and putative divergent functionality of this modification on the two types of RNA. DPred is available as a user-friendly Web server.

The universal dihydrouridine (D) epitranscriptomic mark results from a reduction of uridine by the Dus family of NADPH-dependent reductases and is typically found within the eponym D-loop of tRNAs. Despite its apparent simplicity, D is structurally unique, with the potential to deeply affect the RNA backbone and many, if not all, RNA-connected processes. The first landscape of its occupancy within the tRNAome was reported 20 years ago. Its potential biological significance was highlighted by observations ranging from a strong bias in its ecological distribution to the predictive nature of Dus enzymes overexpression for worse cancer patient outcomes. The exquisite specificity of the Dus enzymes revealed by a structure-function analyses and accumulating clues that the D distribution may expand beyond tRNAs recently led to the development of new high-resolution mapping methods, including Rho-seq that established the presence of D within mRNAs and led to the demonstration of its critical physiological relevance.

Dihydrouridine (D) is an abundant modified base found in the tRNAs of most living organisms and was recently detected in eukaryotic mRNAs. This base confers significant conformational plasticity to RNA molecules. The dihydrouridine biosynthetic reaction is catalyzed by a large family of flavoenzymes, the dihydrouridine synthases (Dus). So far, only bacterial Dus enzymes and their complexes with tRNAs have been structurally characterized. Understanding the structure-function relationships of eukaryotic Dus proteins has been hampered by the paucity of structural data. Here, we combined extensive phylogenetic analysis with high-precision 3D molecular modeling of more than 30 Dus2 enzymes selected along the tree of life to determine the evolutionary molecular basis of D biosynthesis by these enzymes. Dus2 is the eukaryotic enzyme responsible for the synthesis of D20 in tRNAs and is involved in some human cancers and in the detoxification of β-amyloid peptides in Alzheimer's disease. In addition to the domains forming the canonical structure of all Dus, i.e., the catalytic TIM-barrel domain and the helical domain, both participating in RNA recognition in the bacterial Dus, a majority of Dus2 proteins harbor extensions at both ends. While these are mainly unstructured extensions on the N-terminal side, the C-terminal side extensions can adopt well-defined structures such as helices and beta-sheets or even form additional domains such as zinc finger domains. 3D models of Dus2/tRNA complexes were also generated. This study suggests that eukaryotic Dus2 proteins may have an advantage in tRNA recognition over their bacterial counterparts due to their modularity. [ABSTRACT FROM AUTHOR]

Background. Dihydrouridine (D) is a modified transfer RNA post-transcriptional modification (PTM) that occurs abundantly in bacteria, eukaryotes, and archaea. The D modification assists in the stability and conformational flexibility of tRNA. The D modification is also responsible for pulmonary carcinogenesis in humans. Objective. For the detection of D sites, mass spectrometry and site-directed mutagenesis have been developed. However, both are labor-intensive and time-consuming methods. The availability of sequence data has provided the opportunity to build computational models for enhancing the identification of D sites. Based on the sequence data, the DHU-Pred model was proposed in this study to find possible D sites. Methodology. The model was built by employing comprehensive machine learning and feature extraction approaches. It was then validated using in-demand evaluation metrics and rigorous experimentation and testing approaches. Results. The DHU-Pred revealed an accuracy score of 96.9%, which was considerably higher compared to the existing D site predictors. Availability and Implementation. A user-friendly web server for the proposed model was also developed and is freely available for the researchers. [ABSTRACT FROM AUTHOR]

Background Dihydrouridine (D) is a modified transfer RNA post-transcriptional modification (PTM) that occurs abundantly in bacteria, eukaryotes, and archaea. The D modification assists in the stability and conformational flexibility of tRNA. The D modification is also responsible for pulmonary carcinogenesis in humans. Objective For the detection of D sites, mass spectrometry and site-directed mutagenesis have been developed. However, both are labor-intensive and time-consuming methods. The availability of sequence data has provided the opportunity to build computational models for enhancing the identification of D sites. Based on the sequence data, the DHU-Pred model was proposed in this study to find possible D sites. Methodology The model was built by employing comprehensive machine learning and feature extraction approaches. It was then validated using in-demand evaluation metrics and rigorous experimentation and testing approaches. Results The DHU-Pred revealed an accuracy score of 96.9%, which was considerably higher compared to the existing D site predictors. Availability and Implementation A user-friendly web server for the proposed model was also developed and is freely available for the researchers.

Dihydrouridine (D) is an abundant post-transcriptional modification present in transfer RNA from eukaryotes, bacteria, and archaea. D has contributed to treatments for cancerous diseases. Therefore, the precise detection of D modification sites can enable further understanding of its functional roles. Traditional experimental techniques to identify D are laborious and time-consuming. In addition, there are few computational tools for such analysis. In this study, we utilized eleven sequence-derived feature extraction methods and implemented five popular machine algorithms to identify an optimal model. During data preprocessing, data were partitioned for training and testing. Oversampling was also adopted to reduce the effect of the imbalance between positive and negative samples. The best-performing model was obtained through a combination of random forest and nucleotide chemical property modeling. The optimized model presented high sensitivity and specificity values of 0.9688 and 0.9706 in independent tests, respectively. Our proposed model surpassed published tools in independent tests. Furthermore, a series of validations across several aspects was conducted in order to demonstrate the robustness and reliability of our model. [ABSTRACT FROM AUTHOR]

The universal dihydrouridine (D) epitranscriptomic mark results from a reduction of uridine by the Dus family of NADPH-dependent reductases and is typically found within the eponym D-loop of tRNAs. Despite its apparent simplicity, D is structurally unique, with the potential to deeply affect the RNA backbone and many, if not all, RNA-connected processes. The first landscape of its occupancy within the tRNAome was reported 20 years ago. Its potential biological significance was highlighted by observations ranging from a strong bias in its ecological distribution to the predictive nature of Dus enzymes overexpression for worse cancer patient outcomes. The exquisite specificity of the Dus enzymes revealed by a structure-function analyses and accumulating clues that the D distribution may expand beyond tRNAs recently led to the development of new high-resolution mapping methods, including Rho-seq that established the presence of D within mRNAs and led to the demonstration of its critical physiological relevance. [ABSTRACT FROM AUTHOR]

As one of the common post-transcriptional modifications in tRNAs, dihydrouridine (D) has prominent effects on regulating the flexibility of tRNA as well as cancerous diseases. Facing with the expensive and time-consuming sequencing techniques to detect D modification, precise computational tools can largely promote the progress of molecular mechanisms and medical developments. We proposed a novel predictor, called iRNAD_XGBoost, to identify potential D sites using multiple RNA sequence representations. In this method, by considering the imbalance problem using hybrid sampling method SMOTEEEN, the XGBoost-selected top 30 features are applied to construct model. The optimized model showed high Sn and Sp values of 97.13% and 97.38% over jackknife test, respectively. For the independent experiment, these two metrics separately achieved 91.67% and 94.74%. Compared with iRNAD method, this model illustrated high generalizability and consistent prediction efficiencies for positive and negative samples, which yielded satisfactory MCC scores of 0.94 and 0.86, respectively. It is inferred that the chemical property and nucleotide density features (CPND), electron-ion interaction pseudopotential (EIIP and PseEIIP) as well as dinucleotide composition (DNC) are crucial to the recognition of D modification. The proposed predictor is a promising tool to help experimental biologists investigate molecular functions. [ABSTRACT FROM AUTHOR]

Dihydrouridine (D) is a tRNA-modified base conserved throughout all kingdoms of life and assuming an important structural role. The conserved dihydrouridine synthases (Dus) carries out D-synthesis. DusA, DusB and DusC are bacterial members, and their substrate specificity has been determined in Escherichia coli. DusA synthesizes D20/D20a while DusB and DusC are responsible for the synthesis of D17 and D16, respectively. Here, we characterize the function of the unique dus gene encoding a DusB detected in Mollicutes, which are bacteria that evolved from a common Firmicute ancestor via massive genome reduction. Using in vitro activity tests as well as in vivo E. coli complementation assays with the enzyme from Mycoplasma capricolum (DusBMCap), a model organism for the study of these parasitic bacteria, we show that, as expected for a DusB homolog, DusBMCap modifies U17 to D17 but also synthetizes D20/D20a combining therefore both E. coli DusA and DusB activities. Hence, this is the first case of a Dus enzyme able to modify up to three different sites as well as the first example of a tRNA-modifying enzyme that can modify bases present on the two opposite sides of an RNA-loop structure. Comparative analysis of the distribution of DusB homologs in Firmicutes revealed the existence of three DusB subgroups namely DusB1, DusB2 and DusB3. The first two subgroups were likely present in the Firmicute ancestor, and Mollicutes have retained DusB1 and lost DusB2. Altogether, our results suggest that the multisite specificity of the M. capricolum DusB enzyme could be an ancestral property. [ABSTRACT FROM AUTHOR]

Dihydrouridine (D) is an abundant modified base found in the tRNAs of most living organisms and was recently detected in eukaryotic mRNAs. This base confers significant conformational plasticity to RNA molecules. The dihydrouridine biosynthetic reaction is catalyzed by a large family of flavoenzymes, the dihydrouridine synthases (Dus). So far, only bacterial Dus enzymes and their complexes with tRNAs have been structurally characterized. Understanding the structure-function relationships of eukaryotic Dus proteins has been hampered by the paucity of structural data. Here, we combined extensive phylogenetic analysis with high-precision 3D molecular modeling of more than 30 Dus2 enzymes selected along the tree of life to determine the evolutionary molecular basis of D biosynthesis by these enzymes. Dus2 is the eukaryotic enzyme responsible for the synthesis of D20 in tRNAs and is involved in some human cancers and in the detoxification of β-amyloid peptides in Alzheimer’s disease. In addition to the domains forming the canonical structure of all Dus, i.e., the catalytic TIM-barrel domain and the helical domain, both participating in RNA recognition in the bacterial Dus, a majority of Dus2 proteins harbor extensions at both ends. While these are mainly unstructured extensions on the N-terminal side, the C-terminal side extensions can adopt well-defined structures such as helices and beta-sheets or even form additional domains such as zinc finger domains. 3D models of Dus2/tRNA complexes were also generated. This study suggests that eukaryotic Dus2 proteins may have an advantage in tRNA recognition over their bacterial counterparts due to their modularity.

Dihydrouridine (DHU, D) is one of the most abundant post-transcriptional uridine modifications found in tRNA, mRNA, and snoRNA, closely associated with disease pathogenesis and various biological processes in eukaryotes. Identifying D sites is important for understanding the modification mechanisms and/or epigenetic regulation. However, biological experiments for detecting D sites are time-consuming and expensive. Given these challenges, computational methods have been developed for accurately identifying the D sites in genome-wide datasets. However, existing methods have some limitations, and their prediction performance needs to be improved. In this work, we have developed a new computational predictor for accurately identifying D sites called Stack-DHUpred. Briefly, we trained 66 baseline models or single-feature models by connecting six machine learning classifiers with eleven different feature encoding methods and stacked different baseline models to build stacked ensemble learning models. Subsequently, the optimal combination of the baseline models was identified for the construction of the final stacked model. Remarkably, the Stack-DHUpred outperformed the existing predictors on our new independent dataset, indicating that the stacking approach significantly improved the prediction performance. We have made Stack-DHUpred available to the public through a web server (http://kurata35.bio.kyutech.ac.jp/Stack-DHUpred) and a standalone program (https://github.com/kuratahiroyuki/Stack-DHUpred). We believe that Stack-DHUpred will be a valuable tool for accelerating the discovery of D modifications and understanding their role in post-transcriptional regulation., Competing Interests: Declaration of competing interest All authors declare that they have no conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Nucleotide modifications are occurrent in all types of RNA and play an important role in RNA structure formation and stability. Modified bases not only possess the ability to shift the RNA structure ensemble towards desired functional confirmations. By changes in the base pairing partner preference, they may even enlarge or reduce the conformational space, i.e., the number and types of structures the RNA molecule can adopt. However, most methods to predict RNA secondary structure do not provide the means to include the effect of modifications on the result. With the help of a heavily modified transfer RNA (tRNA) molecule, this chapter demonstrates how to include the effect of different base modifications into secondary structure prediction using the ViennaRNA Package. The constructive approach demonstrated here allows for the calculation of minimum free energy structure and suboptimal structures at different levels of modified base support. In particular we, show how to incorporate the isomerization of uridine to pseudouridine ( Ψ ) and the reduction of uridine to dihydrouridine (D)., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Dihydrouridination is one of the abundant modifications in tRNA editing. The presence of dihydrouridine is attributed to tRNA stability desired for the efficient gene translation process. The conversion of uridine to dihydrouridine is catalyzed by flavine containing enzyme called dihydrouridine synthase (Dus). We report first ever information about DusA enzyme from Pseudomonas aeruginosa in form of structural and functional studies. The gene coding for DusA from P. aeruginosa (PADusA) was cloned, expressed and purified, using recombinant DNA technology methods. Thermal and chemical stability of PADusA was determined with respect to temperature and urea-induced equilibrium unfolding experiments, with monitoring the change of ellipticity at 200–260 nm by Circular Dichroism (CD) spectroscopy. Unfolding studies revealed that PADusA has acquired a stable tertiary structure fold with a T m value of 46.2 °C and C m of 2.7 M for urea. The enzyme contains 43% α-helices and 16% β-strands. The three dimensional structure of PADusA was modeled using insilico methods. In order to understand the mechanism of substrate recognition and catalysis, tRNA and puromycin were docked on PADusA structure and their binding was analyzed. The structural features suggested that PADusA may also form a novel target for structure based drug design of antimicrobial agents. [ABSTRACT FROM AUTHOR]

Introduction: Dihydrouridine (D) is a conserved modification of tRNA among all three life domains. D modification enhances the flexibility of a single nucleotide base in the spatial structure and is disease- and evolution-associated. Recent studies have also suggested the presence of dihydrouridine on mRNA. Methods: To identify D in epitranscriptome, we provided a prediction framework named "DPred_3S" based on the machine learning approach for three species D epitranscriptome, which used epitranscriptome sequencing data as training data for the first time. Results: The optimal features were evaluated by the F-score and integration of different features; our model achieved area under the receiver operating characteristic curve (AUROC) scores 0.955, 0.946, and 0.905 for Saccharomyces cerevisiae , Escherichia coli , and Schizosaccharomyces pombe , respectively. The performances of different machine learning algorithms were also compared in this study. Discussion: The high performances of our model suggest the D sites can be distinguished based on their surrounding sequence, but the lower performance of cross-species prediction may be limited by technique preferences., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ren, Chen, Zhang, Shi and Wu.)

All organisms contain RNA modifications in their ribosomal RNA (rRNA), but the importance, positions and exact function of these are still not fully elucidated. Various functions such as stabilizing structures, controlling ribosome assembly and facilitating interactions have been suggested and in some cases substantiated. Bacterial rRNA contains much fewer modifications than eukaryotic rRNA. The rRNA modification patterns in bacteria differ from each other, but too few organisms have been mapped to draw general conclusions. This study maps 23S ribosomal RNA modifications in Clostridium sporogenes that can be characterized as a non-toxin producing Clostridium botulinum. Clostridia are able to sporulate and thereby survive harsh conditions, and are in general considered to be resilient to antibiotics. Selected regions of the 23S rRNA were investigated by mass spectrometry and by primer extension analysis to pinpoint modified sites and the nature of the modifications. Apparently, C. sporogenes 23S rRNA contains few modifications compared to other investigated bacteria. No modifications were identified in domain II and III of 23S rRNA. Three modifications were identified in domain IV, all of which have also been found in other organisms. Two unusual modifications were identified in domain V, methylated dihydrouridine at position U2449 and dihydrouridine at position U2500 (Escherichia coli numbering), in addition to four previously known modified positions. The enzymes responsible for the modifications were searched for in the C. sporogenes genome using BLAST with characterized enzymes as query. The search identified genes potentially coding for RNA modifying enzymes responsible for most of the found modifications. [ABSTRACT FROM AUTHOR]

As an abundant post-transcriptional modification, dihydrouridine (D) has been found in transfer RNA (tRNA) from bacteria, eukaryotes, and archaea. Nonetheless, knowledge of the exact biochemical roles of dihydrouridine in mediating tRNA function is still limited. Accurate identification of the position of D sites is essential for understanding their functions. Therefore, it is desirable to develop novel methods to identify D sites. In this study, an ensemble classifier was proposed for the detection of D modification sites in the Saccharomyces cerevisiae transcriptome by using heterogeneous features. The jackknife test results demonstrate that the proposed predictor is promising for the identification of D modification sites. It is anticipated that the proposed method can be widely used for identifying D modification sites in tRNA.

Transfer RNAs (tRNAs) are central players in translation, functioning as adapter molecules between the informational level of nucleic acids and the functional level of proteins. They show a highly conserved secondary and tertiary structure and the highest density of post-transcriptional modifications among all RNAs. These modifications concentrate in two hotspots-the anticodon loop and the tRNA core region, where the D- and T-loop interact with each other, stabilizing the overall structure of the molecule. These modifications can cause large rearrangements as well as local fine-tuning in the 3D structure of a tRNA. The highly conserved tRNA shape is crucial for the interaction with a variety of proteins and other RNA molecules, but also needs a certain flexibility for a correct interplay. In this context, it was shown that tRNA modifications are important for temperature adaptation in thermophilic as well as psychrophilic organisms, as they modulate rigidity and flexibility of the transcripts, respectively. Here, we give an overview on the impact of modifications on tRNA structure and their importance in thermal adaptation. [ABSTRACT FROM AUTHOR]

Transfer RNAs (tRNAs) are central players in translation, functioning as adapter molecules between the informational level of nucleic acids and the functional level of proteins. They show a highly conserved secondary and tertiary structure and the highest density of post-transcriptional modifications among all RNAs. These modifications concentrate in two hotspots—the anticodon loop and the tRNA core region, where the D- and T-loop interact with each other, stabilizing the overall structure of the molecule. These modifications can cause large rearrangements as well as local fine-tuning in the 3D structure of a tRNA. The highly conserved tRNA shape is crucial for the interaction with a variety of proteins and other RNA molecules, but also needs a certain flexibility for a correct interplay. In this context, it was shown that tRNA modifications are important for temperature adaptation in thermophilic as well as psychrophilic organisms, as they modulate rigidity and flexibility of the transcripts, respectively. Here, we give an overview on the impact of modifications on tRNA structure and their importance in thermal adaptation.

Background: Dihydrouridine (D) is one of the most significant uridine modifications that have a prominent occurrence in eukaryotes. The folding and conformational flexibility of transfer RNA (tRNA) can be attained through this modification., Objective: The modification also triggers lung cancer in humans. The identification of D sites was carried out through conventional laboratory methods; however, those were costly and time-consuming. The readiness of RNA sequences helps in the identification of D sites through computationally intelligent models. However, the most challenging part is turning these biological sequences into distinct vectors., Methods: The current research proposed novel feature extraction mechanisms and the identification of D sites in tRNA sequences using ensemble models. The ensemble models were then subjected to evaluation using k-fold cross-validation and independent testing., Results: The results revealed that the stacking ensemble model outperformed all the ensemble models by revealing 0.98 accuracy, 0.98 specificity, 0.97 sensitivity, and 0.92 Matthews Correlation Coefficient. The proposed model, iDHU-Ensem, was also compared with pre-existing predictors using an independent test. The accuracy scores have shown that the proposed model in this research study performed better than the available predictors., Conclusion: The current research contributed towards the enhancement of D site identification capabilities through computationally intelligent methods. A web-based server, iDHU-Ensem, was also made available for the researchers at https://taseersuleman-idhu-ensem-idhu-ensem.streamlit.app/., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Dihydrouridine (D) is a modified pyrimidine nucleotide universally found in viral, prokaryotic, and eukaryotic species. It serves as a metabolic modulator for various pathological conditions, and its elevated levels in tumors are associated with a series of cancers. Precise identification of D sites on RNA is vital for understanding its biological function. A number of computational approaches have been developed for predicting D sites on tRNAs; however, none have considered mRNAs. We present here DPred, the first computational tool for predicting D on mRNAs in yeast from the primary RNA sequences. Built on a local self-attention layer and a convolutional neural network (CNN) layer, the proposed deep learning model outperformed classic machine learning approaches (random forest, support vector machines, etc.) and achieved reasonable accuracy and reliability with areas under the curve of 0.9166 and 0.9027 in jackknife cross-validation and on an independent testing dataset, respectively. Importantly, we showed that distinct sequence signatures are associated with the D sites on mRNAs and tRNAs, implying potentially different formation mechanisms and putative divergent functionality of this modification on the two types of RNA. DPred is available as a user-friendly Web server., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)

In addition to A, C, G and U, RNA contains over 100 additional chemically distinct residues. An abundant modified base frequently found in tRNAs, dihydrouridine (D) has recently been mapped to over 100 positions in mRNAs in yeast and human cells. Multiple highly conserved dihydrouridine synthases associate with and modify mRNA, suggesting there are many D sites yet to be found. Because D alters RNA structure, installation of D in mRNA is likely to effect multiple steps in mRNA metabolism including processing, trafficking, translation, and degradation. Here, we introduce D-seq, a method to chart the D landscape at single nucleotide resolution. The included protocols start with RNA isolation and carry through D-seq library preparation and data analysis. While the protocols below are tailored to map Ds in mRNA, the D-seq method is generalizable to any RNA type of interest, including non-coding RNAs, which have also recently been identified as dihydrouridine synthase targets., (Copyright © 2023. Published by Elsevier Inc.)

Background: In past number of methods have been developed for predicting post-translational modifications in proteins. In contrast, limited attempt has been made to understand post-transcriptional modifications. Recently it has been shown that tRNA modifications play direct role in the genome structure and codon usage. This study is an attempt to understand kingdom-wise tRNA modifications particularly uridine modifications (UMs), as majority of modifications are uridine-derived. Results: A three-steps strategy has been applied to develop an efficient method for the prediction of UMs. In the first step, we developed a common prediction model for all the kingdoms using a dataset from MODOMICS-2008. Support Vector Machine (SVM) based prediction models were developed and evaluated by five-fold cross-validation technique. Different approaches were applied and found that a hybrid approach of binary and structural information achieved highest Area under the curve (AUC) of 0.936. In the second step, we used newly added tRNA sequences (as independent dataset) of MODOMICS-2012 for the kingdom-wise prediction performance evaluation of previously developed (in the first step) common model and achieved performances between the AUC of 0.910 to 0.949. In the third and last step, we used different datasets from MODOMICS-2012 for the kingdom-wise individual prediction models development and achieved performances between the AUC of 0.915 to 0.987. Conclusions: The hybrid approach is efficient not only to predict kingdom-wise modifications but also to classify them into two most prominent UMs: Pseudouridine (Y) and Dihydrouridine (D). A webserver called tRNAmod (http://crdd. osdd.net/raghava/trnamod/) has been developed, which predicts UMs from both tRNA sequences and whole genome. [ABSTRACT FROM AUTHOR]

Dihydrouridine (D) is one of the most widely conserved tRNA modifications. Dihydrouridine synthase (Dus) is responsible for introducing D modifications into RNA by the reduction of uridine. Recently, a unique substrate-recognition mechanism using a small adapter molecule has been proposed for Thermus thermophilus Dus ( TthDusC). To acquire insight regarding its substrate-recognition mechanism, the crystal structure of DusC from Escherichia coli ( EcoDusC) was determined at 2.1 Å resolution. EcoDusC was shown to be composed of two domains: an N-terminal catalytic domain and a C-terminal tRNA-binding domain. An L-shaped electron density surrounded by highly conserved residues was found in the active site, as observed for TthDus. Structure comparison with TthDus indicated that the N-terminal region has a similar structure, whereas the C-terminal domain has marked differences in its relative orientation to the N-terminal domain as well as in its own structure. These observations suggested that Dus proteins adopt a common substrate-recognition mechanism using an adapter molecule, whereas the manner of tRNA binding is diverse. [ABSTRACT FROM AUTHOR]

The epitranscriptome has emerged as a new fundamental layer of control of gene expression. Nevertheless, the determination of the transcriptome-wide occupancy and function of RNA modifications remains challenging. Here we have developed Rho-seq, an integrated pipeline detecting a range of modifications through differential modification-dependent rhodamine labeling. Using Rho-seq, we confirm that the reduction of uridine to dihydrouridine (D) by the Dus reductase enzymes targets tRNAs in E. coli and fission yeast. We find that the D modification is also present on fission yeast mRNAs, particularly those encoding cytoskeleton-related proteins, which is supported by large-scale proteome analyses and ribosome profiling. We show that the α-tubulin encoding mRNA nda2 undergoes Dus3-dependent dihydrouridylation, which affects its translation. The absence of the modification on nda2 mRNA strongly impacts meiotic chromosome segregation, resulting in low gamete viability. Applying Rho-seq to human cells revealed that tubulin mRNA dihydrouridylation is evolutionarily conserved. [Display omitted] • Rho-seq detects a range of RNA modifications through differential rhodamine labeling • The dihydrouridine tRNA modification is also present on fission yeast mRNAs • Dihydrouridine on the tubulin mRNA is required for meiotic chromosome segregation • Tubulin mRNA dihydrouridylation is evolutionarily conserved from yeast to human Finet et al. report Rho-seq, a new method to determine the transcriptome occupancy of a range of RNA modifications. Rho-seq reveals that the dihydrouridine tRNA modification expands to a subset of fission yeast mRNAs. The modification of the tubulin mRNA is essential for proper meiotic chromosome segregation and gamete viability. [ABSTRACT FROM AUTHOR]

Abstract: Dihydrouridine is one of the most abundant modified bases in tRNA. However, little is known concerning the biochemistry of dihydrouridine synthase (DUS) enzymes. To identify molecular determinants that are necessary for DUS activity, we have developed a DUS-complementation assay in Escherichia coli. Using this assay, we have identified amino-acid residues that are critical for the activity of YjbN, an E. coli DUS. We also show that the aq1598 gene product, a putative DUS from Aquifex aeolicus, catalyzes dihydrouridine formation, providing the first biochemical demonstration that A. aeolicus encodes an active DUS. [Copyright &y& Elsevier]